class_docstr: Test MLlib algorithms on plain lists, to make sure they're passed through
  as NumPy arrays.
code: def test_bisecting_kmeans(self):
      from pyspark.mllib.clustering import BisectingKMeans
  
      data = array([0.0, 0.0, 1.0, 1.0, 9.0, 8.0, 8.0, 9.0]).reshape(
          4, 2
      )
      bskm = BisectingKMeans()
      model = bskm.train(self.sc.parallelize(data, 2), k=4)
      p = array([0.0, 0.0])
      rdd_p = self.sc.parallelize([p])
      self.assertEqual(model.predict(p), model.predict(rdd_p).first())
      self.assertEqual(model.computeCost(p), model.computeCost(rdd_p))
      self.assertEqual(model.k, len(model.clusterCenters))
  
imports: import os
  import tempfile
  from shutil import rmtree
  import unittest
  from numpy import array, array_equal
  from py4j.protocol import Py4JJavaError
  from pyspark.mllib.fpm import FPGrowth
  from pyspark.mllib.recommendation import Rating
  from pyspark.mllib.regression import LabeledPoint
  from pyspark.serializers import CPickleSerializer
  from pyspark.testing.mllibutils import MLlibTestCase
  from pyspark.testing.utils import eventually
label_desc: Use image node_id 1 for calling the ListTests obj's underlying member method code with example usage: obj.test_bisecting_kmeans() without return types